Description

For a data set of features and samples, the classification process is run.
It consists of data transformation, feature selection, classifier training
and testing (prediction of samples not used in training).

Arguments

measurements

Either a matrix, DataFrame or
MultiAssayExperiment containing the training data.
For a matrix, the rows are features, and the columns are samples.
The sample identifiers must be present as column names of the matrix
or the row names of the DataFrame.

classes

Either a vector of class labels of class factor of the same length
as the number of samples in measurements or if the measurements are
of class DataFrame a character vector of length 1 containing the
column name in measurement is also permitted. Not used if measurements
is a MultiAssayExperiment object.

featureSets

An object of type FeatureSetCollection which defines sets of
features or sets of edges.

metaFeatures

Either NULL or a DataFrame which has meta-features
of the numeric data of interest.

minimumOverlapPercent

If featureSets stores sets of features, the minimum overlap
of feature IDs with measurements for a feature set to be retained
in the analysis. If featureSets stores sets of network edges,
the minimum percentage of edges with both vertex IDs found in measurements
that a set has to have to be retained in the analysis.

targets

If measurements is a MultiAssayExperiment, the names of the
data tables to be used. "clinical" is also a valid value and specifies that
numeric variables from the clinical data table will be used.

...

Variables not used by the matrix nor the MultiAssayExperiment method which
are passed into and used by the DataFrame method.

datasetName

A name associated with the data set used.

classificationName

A name associated with the classification.

training

A vector which specifies the training samples.

testing

A vector which specifies the test samples.

params

A list of objects of class of TransformParams,
SelectParams, TrainParams, or PredictParams.
The order they are in the list determines the order in which the stages
of classification are done in.

verbose

Default: 1. A number between 0 and 3 for the amount of progress messages to give.
A higher number will produce more messages as more lower-level functions
print messages.

.iteration

Not to be set by a user. This value is used to keep track of the cross-validation
iteration, if called by runTests.

Details

This function only performs one classification and prediction. See runTests
for a driver function that enables a number of different cross-validation schemes to be applied
and uses this function to perform each iteration. datasetName and classificationName
need to be provided.

Value

If called directly by the user rather than being used internally by runTests, a
SelectResult object.